New Approach for Simulating Reinforced Concrete Walls in Quasi-static Loading
Bibliographic record
Abstract
The main objective of this article is to apply a simplified model to simulate the overall behavior of a reinforced concrete wall without the need to explicitly represent the reinforcing bars in the model nor the progressive degradations of the concrete in tension. The model takes into account the fictitious laws of the material, in order to estimate the capacity of the studied model and its performance to simulate the complex behavior of concrete. The law of the fictitious behavior of reinforced concrete tie rods is based on the shape of the adhesion curve between steel and concrete. Relationships covering the cracking stage up to the elastic limit of steel are proposed according to the properties of concrete and steel materials, the percentage of steel. An analytical computational model is then implemented in the Matlab programming language. Necessary transformations for the integration of the law of fictitious average behavior of steel in the Abaqus software were carried out thus making it possible to make a considerable advance from the point of view of validation of the developed law. The general formulation of the tension law applies to sections where the reinforcements are distributed so that the resistance of the entire section is mobilized. Hence the need to introduce an effective area around the rebars for the application of the fictitious tension law to reinforced concrete walls. Numerical simulations have been validated using an example of reinforced concrete wall subjected to a quasi-static loading. Load-displacement responses are compared and the numerical results approaches well the experimental one. By using the law of the fictitious diagram of the concrete and by defining the effective tensile zone of the wall, the model makes it possible to save a considerable time of calculation compared to a traditional calculation in EF on Abaqus. Doi: 10.28991/cej-2020-03091622 Full Text: PDF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".